Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
This study proposes a simple deep learning-based framework for image-level anti-personnel landmine detection in long-wave infrared imagery. To address challenges posed by the limited size of the available dataset and the small spatial size of anti-personnel landmines within images, we integrate two...
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| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8613 |
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| author | Jun-Hyung Kim Goo-Rak Kwon |
| author_facet | Jun-Hyung Kim Goo-Rak Kwon |
| author_sort | Jun-Hyung Kim |
| collection | DOAJ |
| description | This study proposes a simple deep learning-based framework for image-level anti-personnel landmine detection in long-wave infrared imagery. To address challenges posed by the limited size of the available dataset and the small spatial size of anti-personnel landmines within images, we integrate two key techniques: transfer learning using pre-trained vision foundation models, and attention-based multiple instance learning to derive discriminative image features. We evaluate five pre-trained models, including ResNet, ConvNeXt, ViT, OpenCLIP, and InfMAE, in combination with attention-based multiple instance learning. Furthermore, to mitigate the reliance of trained models on irrelevant features such as artificial or natural structures in the background, we introduce an inpainting-based image augmentation method. Experimental results, conducted on a publicly available “legbreaker” anti-personnel landmine infrared dataset, demonstrate that the proposed framework achieves high precision and recall, validating its effectiveness for landmine detection in infrared imagery. Additional experiments are also performed on an aerial image dataset designed for detecting small-sized ship targets to further validate the effectiveness of the proposed approach. |
| format | Article |
| id | doaj-art-0e8cbc9df38b4ec181c3bfe6ac645327 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0e8cbc9df38b4ec181c3bfe6ac6453272025-08-20T03:36:38ZengMDPI AGApplied Sciences2076-34172025-08-011515861310.3390/app15158613Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared ImagesJun-Hyung Kim0Goo-Rak Kwon1Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of KoreaDepartment of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of KoreaThis study proposes a simple deep learning-based framework for image-level anti-personnel landmine detection in long-wave infrared imagery. To address challenges posed by the limited size of the available dataset and the small spatial size of anti-personnel landmines within images, we integrate two key techniques: transfer learning using pre-trained vision foundation models, and attention-based multiple instance learning to derive discriminative image features. We evaluate five pre-trained models, including ResNet, ConvNeXt, ViT, OpenCLIP, and InfMAE, in combination with attention-based multiple instance learning. Furthermore, to mitigate the reliance of trained models on irrelevant features such as artificial or natural structures in the background, we introduce an inpainting-based image augmentation method. Experimental results, conducted on a publicly available “legbreaker” anti-personnel landmine infrared dataset, demonstrate that the proposed framework achieves high precision and recall, validating its effectiveness for landmine detection in infrared imagery. Additional experiments are also performed on an aerial image dataset designed for detecting small-sized ship targets to further validate the effectiveness of the proposed approach.https://www.mdpi.com/2076-3417/15/15/8613transfer learningmultiple instance learningspurious featureinfraredsmall target |
| spellingShingle | Jun-Hyung Kim Goo-Rak Kwon Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images Applied Sciences transfer learning multiple instance learning spurious feature infrared small target |
| title | Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images |
| title_full | Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images |
| title_fullStr | Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images |
| title_full_unstemmed | Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images |
| title_short | Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images |
| title_sort | image level anti personnel landmine detection using deep learning in long wave infrared images |
| topic | transfer learning multiple instance learning spurious feature infrared small target |
| url | https://www.mdpi.com/2076-3417/15/15/8613 |
| work_keys_str_mv | AT junhyungkim imagelevelantipersonnellandminedetectionusingdeeplearninginlongwaveinfraredimages AT goorakkwon imagelevelantipersonnellandminedetectionusingdeeplearninginlongwaveinfraredimages |